人工智能和机器人在手术极端环境下

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Wenji Li, Lihong Xie, C. Sivaparthipan, C. Vignesh
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引用次数: 0

摘要

与其他复杂手术的标准技术相比,机器人手术为外科医生提供了更高程度的准确性、多功能性和可控性。机器人手术技术为患者提供了许多优势,并且当使用这种复杂的交互式设备进行治疗时,会导致更容易预测的不可预见的效果。在机器人手术过程中发生的具有挑战性的并发症包括操作机器人系统时人为错误的风险和机械故障的可能性。本文提出了机器人辅助远程中心手术系统(RA-RCSS),通过人工专家的实践反馈和演示来提高外科医生的机械故障威胁和实践技能。训练基于掩模区域的监督学习模型,对手术器械和手术目标进行语义分割,进一步提高手术坐标,便于自主实践。此外,将主从双边技术与RA-RCSS相结合,对机器人系统的机械故障进行分析。新兴的安全标准环境被认为是自主手术机器人商业化的关键促成因素。从精度、安全性、性能和成本等方面进行了仿真分析,验证了所提框架的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI with robotics in surgery extreme environments
Robotic surgery offers surgeons a greater degree of accuracy, versatility, and control than with standard techniques for other kinds of complicated procedures. The robotic surgery technology offers numerous advantages for patients and leads to unforeseen effects that are easier to predict when such a complex interactive device is used for treatment. The challenging complications that are occurred during robotic surgery include, risk of human error while operating the robotic system and the possibility for mechanical failure. The paper proposes Robot Assisted - Remote Center Surgical System (RA-RCSS) to improve mechanical malfunction threat and practical skills of surgeons through intra practice feedback and demonstration from human experts. A mask region-based supervised learning model is trained to conduct semantic segmentation of surgical instruments and targets to improve surgical coordinates further and to facilitate self-oriented practice. Furthermore, the master-slave bilateral technique is integrated with RA-RCSS to analyze the mechanical failures and malfunctions of the robotic system. The emerging safety standard environment is presented as a key enabling factor in the commercialization of autonomous surgical robots. The simulation analysis is performed based on accuracy, security, performance, and cost factor proves the reliability of the proposed framework.
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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